The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
State of the art in pattern recognition
Abstract
This paper reviews statistical, adaptive, and heuristic techniques used in laboratory investigations of pattern recognition problems. The discussion includes correlation methods, discriminant analysis, maximum likelihood decisions minimax techniques, perceptron-like algorithms, feature extraction, preprocessing, clustering and nonsupervised learning. Two-dimensional distributions are used to illustrate the properties of the various procedures. Several experimental projects, representative of prospective applications, are also described.
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Citations
Cites
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
An algorithm for the machine calculation of complex Fourier series
Cooley, J. W., & Tukey, J. W. (1965) · Mathematics of Computation · doi:10.1090/s0025-5718-1965-0178586-1 · Cited by 13,154
Classification into two Multivariate Normal Distributions with Different Covariance Matrices
Anderson, T. W., & Bahadur, R. R. (1962) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177704568 · Cited by 248
Metadata
- Authors
- Nagy, G.
- Venue
- Proceedings of the IEEE
- Published
- 1968
- Volume
- 56
- Issue
- 5
- Pages
- 836-863
- ISSN
- 0018-9219 (ISSN-L)
- OpenAlex
- W2067958620
- Cited by
- 293
- References
- 3
- FWCI
- 42.42
- Type
- journal article
- Language
- en
- Open access
- closed
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- Nagy, G. (1968). State of the art in pattern recognition. Proceedings of the IEEE. https://doi.org/10.1109/proc.1968.6414
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
